Wavelength tunable resonant phase-change synaptic weights for photonic neuromorphic computing

The prohibitively large inclusions of micro-ring resonators, interconnected waveguide crossbar arrays, and multi-port multi-mode interferometers components demonstrated in the latest integrated photonic neural network hardware daccelerators, pose a significant scaling issue for implementing large number of neural connections required to accurately represent the cognitive functions of a biological brain. In this investigation we utilize phase-change chalcogenide material GST to replicate a non-volatile synaptic weight with built-in memory functionality by employing metamaterial design principles for wavelength-division multiplexing photonic architectures. The transmission response of the optimized GST metamaterial gives rise to contrast ratios of 6dB in both positive and negative weighting values.